generated from kgod/ai-review-template
200 lines
8.5 KiB
Python
200 lines
8.5 KiB
Python
"""LLM rescue for Builder messages the keyword routing drops to the generic fallback.
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Active only when RESUME_AGENT_INTENT_ROUTER_MODE=on and an LLM provider is configured.
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The rescue never *replaces* keyword routing — it only handles messages that already
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fell through every keyword rule (the path that used to answer "可以。接下来建议…").
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Classification failures and low confidence decline to the legacy fallback turn.
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"""
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from __future__ import annotations
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import re
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from typing import Any
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from ..chat_intent_classifier import build_chat_intent_classifier, build_chat_state_summary
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from ..chat_intents import ChatIntent
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from ..fsm import Transition, assistant_turn
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from ..llm_services import log_ai_event
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from ..models import ComposerMode, Stage
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from ..settings import load_settings
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from .constants import SECTION_HEADINGS, SECTION_KEYWORDS
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from .followups import _redisplay_revision_candidate, _regenerate_entry_candidate
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from .predicates import _entry_by_id
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from .state import _dedupe_strings, _gap_state, _reset_gap_state, _set_stream_phases, ensure_builder_state
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from .turns import _record_card
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_CLASSIFIER_UNSET = object()
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_MIN_RESCUE_CONFIDENCE = 0.5
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_ENTRY_LABEL_KEYS = ("company", "project_name", "school", "organization", "title", "name", "position", "role")
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_DETAIL_ACK = "收到。这段经历还没整理完：请继续补充具体事实，或回复「没有」/「跳过」略过当前问题。"
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def _cached_classifier(agent: Any) -> Any:
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classifier = getattr(agent, "_chat_intent_classifier", _CLASSIFIER_UNSET)
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if classifier is _CLASSIFIER_UNSET:
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settings = load_settings()
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classifier = (
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build_chat_intent_classifier(settings)
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if settings.intent_router_mode == "on" and settings.use_openai
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else None
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)
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agent._chat_intent_classifier = classifier
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return classifier
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def _squash(value: str) -> str:
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return re.sub(r"[\s,，。；;！!？?]", "", value.casefold())
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def _find_entry_by_hint(resume_content: dict[str, Any], hint: str | None) -> tuple[dict[str, Any], dict[str, Any]] | None:
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needle = _squash(hint or "")
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if not needle:
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return None
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for section in resume_content.get("sections") or []:
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if not isinstance(section, dict):
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continue
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for entry in section.get("items") or []:
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if not isinstance(entry, dict):
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continue
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for key in _ENTRY_LABEL_KEYS:
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label = _squash(str(entry.get(key) or ""))
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if label and (label in needle or needle in label):
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return section, entry
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return None
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def _section_hint(content: str, raw: str | None) -> str | None:
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"""Section the user named, derived deterministically from the message.
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The LLM classifier is not prompted to fill target_section for edit intents
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and may emit a Chinese heading when it does — normalize that, then fall back
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to matching the message itself (full "项目经历" outranks bare tokens like
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"项目"). Never trust the classifier alone: a null/wrong section used to drop
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the routing to the most-recent entry.
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"""
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value = (raw or "").strip().casefold()
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if len(value) >= 2:
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for kind, heading in SECTION_HEADINGS.items():
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if value == kind or heading.casefold().startswith(value):
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return kind
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normalized = content.casefold()
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for kind, heading in SECTION_HEADINGS.items():
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if heading in normalized:
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return kind
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return next((kind for kind, tokens in SECTION_KEYWORDS.items() if any(token in normalized for token in tokens)), None)
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def _rescue_target(
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profile: dict[str, Any],
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resume_content: dict[str, Any],
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hint: str | None,
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target_section: str | None = None,
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) -> tuple[dict[str, Any], dict[str, Any]] | None:
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target = _find_entry_by_hint(resume_content, hint)
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if target is not None:
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return target
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if target_section:
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# A section the user named explicitly outranks the most-recent-entry
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# fallback; without this, "优化教育经历" lands on whatever was confirmed
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# last (e.g. a campus entry).
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section_entries = [
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(section, entry)
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for section in resume_content.get("sections") or []
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if isinstance(section, dict) and str(section.get("kind") or "") == target_section
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for entry in section.get("items") or []
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if isinstance(entry, dict)
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]
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if len(section_entries) == 1:
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return section_entries[0]
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reference = ensure_builder_state(profile).get("last_confirmed_entry")
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if isinstance(reference, dict):
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return _entry_by_id(resume_content, str(reference.get("entry_id") or ""))
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return None
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def llm_detail_route(agent: Any, profile: dict[str, Any], content: str) -> Transition | None:
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"""LLM gate before free text is merged into the active draft as facts.
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Only intents that must NOT be merged are intercepted; provide_facts and
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anything uncertain return None so the legacy merge path continues.
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"""
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try:
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classifier = _cached_classifier(agent)
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if classifier is None:
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return None
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result = classifier.classify(content, state_summary=build_chat_state_summary(profile, ensure_builder_state(profile)))
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except Exception:
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return None
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log_ai_event("chat_intent_detail_route", intent=result.intent.value, confidence=result.confidence)
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if result.confidence < _MIN_RESCUE_CONFIDENCE:
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return None
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if result.intent is ChatIntent.NO_INFO:
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state = ensure_builder_state(profile)
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gap_state = _gap_state(state)
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gap_state["skipped"] = _dedupe_strings([*gap_state["skipped"], *gap_state["asked"]])
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from .flow import _process_detail_message # late import: flow imports this module
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return _process_detail_message(agent, profile, "")
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if result.intent is ChatIntent.REVISE_PROPOSAL:
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return _redisplay_revision_candidate(agent, profile, result.revision_instruction or content)
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if result.intent in {ChatIntent.CHITCHAT, ChatIntent.ASK_QUESTION}:
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_set_stream_phases(profile, "structuring")
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return Transition(
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Stage.BUILDER_CONVERSATION,
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profile,
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assistant_turn(_DETAIL_ACK, [], mode=ComposerMode.CHAT),
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)
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return None
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def llm_intent_rescue(
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agent: Any, profile: dict[str, Any], content: str, resume_content: dict[str, Any]
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) -> Transition | None:
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"""Classify a fell-through message and route it, or None to keep the legacy turn."""
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try:
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classifier = _cached_classifier(agent)
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if classifier is None:
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return None
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result = classifier.classify(content, state_summary=build_chat_state_summary(profile, ensure_builder_state(profile)))
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except Exception:
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return None
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log_ai_event(
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"chat_intent_rescue",
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intent=result.intent.value,
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confidence=result.confidence,
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rescued=result.confidence >= _MIN_RESCUE_CONFIDENCE
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and result.intent in {ChatIntent.EDIT_ENTRY, ChatIntent.REVISE_PROPOSAL, ChatIntent.NEW_ENTRY},
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)
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if result.confidence < _MIN_RESCUE_CONFIDENCE:
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return None
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state = ensure_builder_state(profile)
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if result.intent in {ChatIntent.EDIT_ENTRY, ChatIntent.REVISE_PROPOSAL}:
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target = _rescue_target(
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profile, resume_content, result.target_entry_hint, _section_hint(content, result.target_section)
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)
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if target is None:
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return None
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section_data, entry = target
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if str(section_data.get("kind") or "") not in SECTION_HEADINGS:
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return None
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if result.intent is ChatIntent.REVISE_PROPOSAL:
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return _regenerate_entry_candidate(agent, profile, section_data, entry, result.revision_instruction or content)
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from .flow import _begin_edit # late import: flow imports this module
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return _begin_edit(profile, section_data, entry)
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if result.intent is ChatIntent.NEW_ENTRY and result.target_section in SECTION_HEADINGS:
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section = str(result.target_section)
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state["active_section"] = section
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_reset_gap_state(state)
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_set_stream_phases(profile, "suggesting_next", "structuring")
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return Transition(
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Stage.BUILDER_CONVERSATION,
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profile,
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assistant_turn(
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f"好的，先补充{SECTION_HEADINGS[section]}的关键信息。",
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[_record_card(section, title=f"补充{SECTION_HEADINGS[section]}", skippable=True)],
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mode=ComposerMode.CHAT,
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),
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)
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return None
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